ARTIFICIAL NEURAL NETWORK MODELING OF APPLE DRYING PROCESS

Abstract

ABSTRACT Artificial neural network (ANN) modeling and several mathematical models were applied to predict the moisture ratio in an apple drying process. Four drying mathematical models were fitted to the data obtained from eight drying runs and the most accurate model was selected. Two sets of ANN modeling were also performed. In the first set, the data obtained from each pilot were modeled individually to compare the ANN predictions with the best mathematical model. In the second set of ANN modeling, the simultaneous effect of all the four input parameters including air velocity, air temperature, the thickness of apple slices and drying time was investigated. The results showed that the ANN predictions were more accurate in comparison with the best fitted mathematical model. In addition, none of the mathematical models are able to predict the effect of the four input parameters simultaneously, while the presented ANN model predicts this effect with a good precision. PRACTICAL APPLICATIONS Today, modeling of chemical engineering processes is widespread in the process industries. An accurate modeling results in a precise prediction of the products of a process which could be very expensive or even unsafe to evaluate by experimental experiences. Because artificial neural network modeling is more or less proved to be one of the best modelings against mathematical ones, we suggest it to be considered for industrial processes such as drying in the food industry.